fixes: TODO, debug test.clustering.R and finish writing clustering.R

[epclust.git] / epclust / tests / testthat / test.filter.R

diff --git a/epclust/tests/testthat/test.filter.R b/epclust/tests/testthat/test.filter.R
index d94a5ac..9d1916d 100644 (file)
--- a/epclust/tests/testthat/test.filter.R
+++ b/epclust/tests/testthat/test.filter.R
@@ -1,8 +1,18 @@
-TODO: test computeMedoids + filter
-#              #R-equivalent, requiring a matrix (thus potentially breaking "fit-in-memory" hope)
-#              mat_meds = medoids[,]
-#              mi = rep(NA,nb_series)
-#              for (i in 1:nb_series)
-#                      mi[i] <- which.min( rowSums( sweep(mat_meds, 2, ref_series[i,], '-')^2 ) )
-#              rm(mat_meds); gc()
+context("epclustFilter")
 
+#TODO: find a better name
+test_that("[time-]serie filtering behave as expected",
+{
+       # Currently just a mean of 3 values
+       M = matrix(runif(1000,min=-7,max=7), ncol=10)
+       ref_fM = stats::filter(M, c(1/3,1/3,1/3), circular=FALSE)
+       fM = epclust:::.epclustFilter(M)
+
+       #Expect an agreement on all inner values
+       expect_equal(dim(fM), c(100,10))
+       expect_equal(fM[2:99,], ref_fM[,2:99])
+
+       #For border values, just apply a "by-hand" mean
+       expect_equal(fM[1,], colMeans(rbind(M[1,],M[2,],M[100,])))
+       expect_equal(fM[100,], colMeans(rbind(M[100,],M[99,],M[1,])))
+}